Learning Resources

Curated courses, tutorials, papers, and tools — organised by topic. Click any card to explore.

ML Methods

ML Fundamentals

Probability, statistics, sklearn, bias-variance trade-off

Chemical Representations

SMILES, fingerprints, molecular descriptors, RDKit

MLPs, backpropagation, PyTorch, optimisers

Graph Neural Networks

Message passing, GCN, GAT, MPNN, PyTorch Geometric

Self-attention, BERT, ChemBERTa, chemical language models

Generative Models

VAEs, diffusion models, molecular generation, SMILES-RNNs

Bayesian Optimization

Gaussian processes, EI/UCB, BoTorch, multi-objective BO

Active Learning

Uncertainty sampling, BALD, core-set, pool-based loops

Uncertainty Quantification

Calibration, conformal prediction, ensembles, Bayesian NNs

LLMs & Agentic AI

RAG, tool use, ReAct, chemistry agents, self-driving labs

Chemistry Applications

Drug Discovery & QSAR

ADMET, bioactivity, MoleculeNet, ChEMBL, TDC

Materials Science

Formation energy, Materials Project, matminer, OCP

Reaction Prediction & Synthesis

Retrosynthesis, yield prediction, RXN, ASKCOS

Quantum Chemistry & DFT

QM9, ANI-1, ML potentials, PySCF, HOMO-LUMO

Self-Driving Laboratories

Autonomous experiments, Olympus, Acceleration Consortium